Assessment of Malnutrition in Crohn's Disease Patients: A Novel Risk Prediction Model with Dynamic Optimization...

This study aimed to construct and validate a malnutrition risk prediction model combining multivariable logistic regression and machine learning for patients with Crohn's disease (CD), with the goal of improving the precision of malnutrition risk identification through integration of inflammatory markers and disease characteristics. PubMed, Web of Science, Cochrane Library, Embase, and China National Knowledge Infrastructure (CNKI) were systematically searched to identify risk factors associated with malnutrition in patients with CD. High-quality studies using the Global Leadership Initiative on Malnutrition (GLIM) 2019 criteria, European Society for Clinical Nutrition and Metabolism (ESPEN) 2015 criteria, or Malnutrition Universal Screening Tool (MUST) criteria were included in the meta-analysis, while the study cohort applied the ESPEN 2015 criteria exclusively to ensure consistent outcome definition. The prediction model was developed using data from 800 patients with CD from the Inflammatory Bowel Disease Cohort Database (IBDCD) and validated using bootstrap resampling and an independent non-overlapping hold-out subset of 280 patients from the same database. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test, and Brier score. No significant baseline differences were observed between the training and validation cohorts. The model achieved an AUC of 0.987 in the training cohort and 0.967 in the validation cohort, demonstrating good discrimination and calibration. Decision curve analysis further demonstrated clinically meaningful net benefits across relevant threshold probabilities. This model effectively identifies malnutrition risk in patients with CD and may support personalized nutritional intervention, optimize clinical decision-making, and improve patient outcomes and quality of life. Future multicenter studies are required to further validate the model's generalizability and to evaluate the integration of socioeconomic factors for further optimization.